1 Background

Demand for abundant and diverse resources in the oceans is growing, necessitating marine spatial planning. To inform development of Marine Hydrokinetic (MHK) and Offshore Wind (OSW) resources, DOE has asked NREL to identify — and mitigate where possible — the competing uses between MHK/OSW technologies and subsea power/telecoms cables. The first step in this work is to identify and quantify the overlap between the MHK/OSW resource availability and existing cable routes. Several publicly available data layers are available that identify cable routes (e.g. MarineCadastre.gov currently hosts an offshore cables geographical information system (GIS) data layer) and MHK/OSW resource density (MHK Atlas, Wind Prospector). The cable route linear features, however, do not indicate the setback distance necessary to accommodate subsea cable maintenance requirements. Preliminary work was done within NREL to evaluate the influence of subsea cable setback distance on the overlap with MHK/OSW for the west coast of the U.S (Amante, Kilcher, Roberts, & Draxl, 2016). Industry reports (Communications Security, Reliability and Interoperability Council IV, 2014, 2016) from the International Cable Protection Committee (ICPC) of the North American Submarine Cable Association (NASCA; n-a-s-c-a.org) advise on setback distances that inform this analysis.

2 Methods

2.1 Study Area and Submarine Cables

The study area included the 200 nm extent of US waters downloaded from MarineRegions.org1 and overlapping the offshore cable data layer available through MarineCadastre.gov.2 See Figure @ref(fig:map_study-area).

TODO: add citations to bibliography:

See Table @ref(tab:tbl_data-sources).

read_csv('../data/data_sources.csv') %>%
  kable(
    caption = 'Data sources from preliminary report.', booktabs=T)
(#tab:tbl_data-sources)Data sources from preliminary report.
Data Source Website
Offshore Cables National Oceanic and Atmospheric Administration (NOAA) http://marinecadastre.gov/data/
Bathymetry General Bathymetric Chart of the Oceans (GEBCO) http://www.gebco.net/data_and_products/gridded_bathymetry_data/
U.S. Shoreline NOAA http://shoreline.noaa.gov/data/datasheets/medres.html
Tide Georgia Tech Research Corporation http://www1.eere.energy.gov/water/pdfs/1023527.pdf
Wave Electric Power Research Institute http://www1.eere.energy.gov/water/pdfs/mappingandassessment.pdf
Wind (100m height) AWS Truepower, LLC for windNavigator http://apps2.eere.energy.gov/wind/windexchange/windmaps/ 
usa     = read_sf(usa_geo)

usa %>% 
  as_tibble() %>%
  select(eez=Territory1, eez_part=part, area_km2=Area_km2) %>%
  datatable() %>%
  formatRound('area_km2', digits=1)
lns_usa = read_sf(lns_usa_geo)

usa = usa %>%
  filter(part %in% lns_usa$part)
  
pal <- colorFactor(
  colorRampPalette(brewer.pal(11,'Spectral'))(length(usa$part)), 
  usa$part)

leaflet(usa) %>%
  addProviderTiles('Esri.OceanBasemap') %>%
  addPolylines(
    data = lns_usa, group='cables', 
    color='black', opacity = 1, weight=1,
    popup = ~sprintf(
      "scaleBand: %s<br>description: %s<br>effectiveDate: %s",
      scaleBand, description, effectiveDate)) %>%
  addPolygons(
   label = ~sprintf('%s', GeoName),
   color = ~pal(part)) %>%
  setView(-97, 38, zoom=3)

(#fig:map_study-area)Map of NOAA Charted Submarine cables in the United States as of December 2012.

2.2 Depth

The bathymetric depth comes from the GEBCO 30 arc-second grid. Here’s there requested attribution:

GEBCO_2014 Grid, version 20150318, www.gebco.net

2.2.1 Renewable Energy

NREL.

2.3 Separation Zones for Routing New Cables

“ICPC Recommendation 2 No. 10: parallel submarine cables maintain a separation distance of the lesser of 3 times depth of water or (where not achievable) 2 times the depth of water following consultation and agreement between affected parties — a separation standard the principles of which also apply to spacing of submarine cables and other marine infrastructure”

At least 2 separation zones based on depth:

  1. Minimum: 2 times the depth of water

  2. Recommended: 3 times depth of water

Psudocode:

# convert depth (GEBCO 30 sec resolution) to polygons of cells
depth_cells = as.polygon(depth_raster)

# intersect cables with depth cells
cable_cells = intersect(cables_lines, depth_cells)

# buffer based on depth
cables_buffers = list()
for (x in unique(cable_cells$depth)){

  cables_buffers[str(x)] = cable_cells %>%
    subset(depth == x) %>%
    buffer(x)

}
cable_buf = merge(cables_buffers) 

# smooth out jagged edges between cells
cable_buf = simplify(cable_buf)

2.4 Depth-Varying Buffer

A depth-varying buffer to the offshore cables, 2 * depth for “minimum” and 3 * depth for “recommended” separation zones for routing new cables, by first intersecting depth with cables, then iterating over each depth to apply the appropriate buffers before finally dissolving all buffers. In order to apply the buffer, I needed to project from geographic coordinates to a projection that minimizes area distortion, so chose Albers Equal Area and applied the “one-sixth rule” based on the extent of the cable features to minimize distortion.

dx2     = read_sf(dx2_geo)
dx3     = read_sf(dx3_geo)
lns_d1x = read_sf(lns_d1x_geo)

leaflet() %>% 
  addProviderTiles("Stamen.TonerLite", group = "B&W") %>%
  addProviderTiles('Esri.OceanBasemap', group = "Ocean") %>%  #
  addPolygons(
    data = dx2, group='buffer: 3*depth',
    color='red', stroke=F, fillOpacity = 0.5) %>%
  addPolygons(
    data = dx3, group='buffer: 2*depth', 
    color='red', stroke=F, fillOpacity = 0.5) %>%
  addPolylines(
    data = lns_d1x, group='cables', 
    color='black', opacity = 1, weight=1,
    popup = ~sprintf(
      "scaleBand: %s<br>description: %s<br>effectiveDate: %s<br>EEZ: %s<br>EEZ part: %d<br>Buffer (1x): %d",
      scaleBand, description, effectiveDate, GeoName, part, buf1x)) %>%
  addLayersControl(
    baseGroups = c("B&W", "Ocean"),
    overlayGroups = c('buffer: 3*depth', 'buffer: 2*depth', 'cables'),
    options = layersControlOptions(collapsed = FALSE)) %>%
  setView(-97, 38, zoom=3)

Next steps. The above R code works well for this small area, but will not scale well to the national level. I’ll implement the intersection, buffering and dissolve operations in a PostGIS spatial database, which has the advantage of much more efficient spatial indexing and operations.

2.5 Avoidance Zones for Siting New Facilities

“ICPC Recommendation 13 No. 2, which establishes a methodology for determining site-specific proximity limits between submarine cables and offshore wind facilities and a default separation distance in shallower waters of 500 meters on either side of an in-service submarine cable — a separation standard the principles of which also apply to other offshore renewable energy projects.”

“Subsea Cables UK Guideline No. 6 (endorsed by NASCA), which establishes principles for determining safe proximity distances and negotiating proximity agreements between offshore wind farms and submarine cables and reflects extensive experience in the United Kingdom with managing spatial conflicts between offshore wind farms and submarine cables.”

“endorse a default separation distance of 500 meters in water depths of less than 75 meters and the greater of 500 meters or two times the depth of water in greater water depths.”

  1. Default separation distance by depth:
    • <= 250 m: 500 m
    • > 250 m: 2 x depth

Psudocode:

# buffer based on depth
cables_buffers = list()
for (x in unique(cable_cells$depth)){

  cables_buffers[str(x)] = cable_cells %>%
    subset(depth == x) %>%
    buffer(x)

}
cable_buf = merge(cables_buffers) 

# smooth out jagged edges between cells
cable_buf = simplify(cable_buf)

3 Results

Generated by:

Google Earth files (*.kml):

3.1 Overlay with Renewable Energy from NREL

Extract average and area of overlap between zones and renewable energy potential areas, possibly at various depth bins.

3.1.1 Wind

units: wind speed (m/s) at 90m hub height

wind = read_sf(wind_geo)

wind %>% as('Spatial') %>%
  spplot()
wind_cbls = read_csv(wind_cbls_csv)

wind_cbls %>%
  select(-energy_num) %>%
  rename(`speed m/s`=energy_lbl) %>%
  mutate(
   pct_region         = pct_region/100,
   cable2_pct         = cable2_pct/100,
   cable3_pct         = cable3_pct/100,
   cable3_pct_region  = cable3_pct_region/100) %>%
  datatable() %>%
  formatRound(
    c('area_km2','cable2_km2','cable3_km2'), 
    digits=2) %>% 
  formatPercentage(
    c('pct_region','cable2_pct','cable3_pct','cable3_pct_region'), 
    digits=1)

3.1.2 Wave

units: wave energy flux (kW/m)

wave = read_sf(wave_geo)

wave %>% as('Spatial') %>%
  spplot()
wave_cbls = read_csv(wave_cbls_csv)

wave_cbls %>%
  select(-energy_num) %>%
  rename(`flux kW/m`=energy_lbl) %>%
  mutate(
   pct_all        = pct_all/100,
   cable2_pct     = cable2_pct/100,
   cable3_pct     = cable3_pct/100,
   cable3_pct_all = cable3_pct_all/100) %>%
  datatable() %>%
  formatRound(
    c('area_km2','cable2_km2','cable3_km2'), 
    digits=2) %>% 
  formatPercentage(
    c('pct_all','cable2_pct','cable3_pct','cable3_pct_all'), 
    digits=1)

3.1.3 Tidal

units: mean power (W/m^2)

TODO: seperate tif into east and west for display in leaflet

tide = raster(tide_tif)

plot(tide_tif)
tide_cbls = read_csv(tide_cbls_csv)

tide_cbls %>%
  select(-energy_num) %>%
  rename(`power W/m2`=energy_lbl) %>%
  mutate(
   pct_all        = pct_all/100,
   cable2_pct     = cable2_pct/100,
   cable3_pct     = cable3_pct/100,
   cable3_pct_all = cable3_pct_all/100) %>%
  datatable() %>%
  formatRound(
    c('area_km2','cable2_km2','cable3_km2'), 
    digits=2) %>% 
  formatPercentage(
    c('pct_all','cable2_pct','cable3_pct','cable3_pct_all'), 
    digits=1)

3.2 Next Steps

  • stacked histogram by cbl2/(cbl3-cbl2)/other; hist(wind$Speed_90)

  • simplify as native geojson

Musial et al (2016):

  • wind speed (m/s) – Table A-3 (p. 48): <7, 7-8, 8-9, 9-10, 10-11, total

  • depth classes (m) – Table B-1 (p. 49): <30, 30-60, 60-700, 700-1000, >1000, total

  • distance to shore (nm) – Table B-2 (p. 50): <3, 3-12, 12-50, 50-200, total

  • by states

4 Conclusions

4.1 Communication with Stakeholders

Products will be online and readily digestable by stakeholders.

References

Amante, C., Kilcher, L., Roberts, B., & Draxl, C. (2016). Offshore Cable Analysis: Pilot Study.

Communications Security, Reliability and Interoperability Council IV. (2014). Protection of Submarine Cables Through Spatial Separation.

Communications Security, Reliability and Interoperability Council IV. (2016). Clustering of Cables and Cable Landings.

  1. MarineRegions.org US exclusive economic zone (EEZ) data, version World_EEZ_v9_20161021↩

  2. MarineCadastre.gov cable metadata: https://coast.noaa.gov/dataservices/Metadata/TransformMetadata?u=https://coast.noaa.gov/data/Documents/Metadata/harvest/MarineCadastre/NOAAChartedSubmarineCables.xml&f=html↩